6 papers
DMGD: Train-Free Dataset Distillation with Semantic-Distribution Matching in Diffusion Models
Qichao Wang, Yunhong Lu, Hengyuan Cao +2
Dataset distillation enables efficient training by distilling the information of large-scale datasets into significantly smaller synthetic datasets. Diffusion based paradigms have…
Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation
Yunhong Lu, Yanhong Zeng, Haobo Li +9
Efficient streaming video generation is critical for simulating interactive and dynamic worlds. Existing methods distill few-step video diffusion models with sliding window attenti…
OmniTry: Virtual Try-On Anything without Masks
Yutong Feng, Linlin Zhang, Hengyuan Cao +5
Virtual Try-ON (VTON) is a practical and widely-applied task, for which most of existing works focus on clothes. This paper presents OmniTry, a unified framework that extends VTON…
Smoothed Preference Optimization via ReNoise Inversion for Aligning Diffusion Models with Varied Human Preferences
Yunhong Lu, Qichao Wang, Hengyuan Cao +2
Direct Preference Optimization (DPO) aligns text-to-image (T2I) generation models with human preferences using pairwise preference data. Although substantial resources are expended…
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis
Hengyuan Cao, Yutong Feng, Biao Gong +4
Video generative models can be regarded as world simulators due to their ability to capture dynamic, continuous changes inherent in real-world environments. These models integrate…
InPO: Inversion Preference Optimization with Reparametrized DDIM for Efficient Diffusion Model Alignment
Yunhong Lu, Qichao Wang, Hengyuan Cao +3
Without using explicit reward, direct preference optimization (DPO) employs paired human preference data to fine-tune generative models, a method that has garnered considerable att…